Change file format of network graph file

Essential/networkgraphvisual.py

@@ -1,110 +0,0 @@
-import sqlite3
-import pandas as pd
-import numpy as np
-from sklearn.metrics.pairwise import cosine_similarity
-import networkx as nx
-import plotly.graph_objects as go
-import os.path
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-db_path = (current_dir + r"\data\food_data.db")
-
-conn = sqlite3.connect(db_path)
-query = "SELECT * FROM food_data LIMIT 100"
-df = pd.read_sql_query(query, conn)
-
-df= df.fillna(0)
-
-nutrient_columns = [
-    'energy-kcal_100g', 'fat_100g', 'saturated-fat_100g', 'unsaturated-fat_100g',
-    'omega-3-fat_100g', 'omega-6-fat_100g', 'omega-9-fat_100g', 'trans-fat_100g',
-    'cholesterol_100g', 'carbohydrates_100g', 'sugars_100g', 'sucrose_100g',
-    'glucose_100g', 'fructose_100g', 'lactose_100g', 'maltose_100g', 'fiber_100g',
-    'soluble-fiber_100g', 'insoluble-fiber_100g', 'proteins_100g', 'salt_100g',
-    'added-salt_100g', 'sodium_100g', 'alcohol_100g', 'vitamin-a_100g',
-    'beta-carotene_100g', 'vitamin-d_100g', 'vitamin-e_100g', 'vitamin-k_100g',
-    'vitamin-c_100g', 'vitamin-b1_100g', 'vitamin-b2_100g', 'vitamin-pp_100g',
-    'vitamin-b6_100g', 'vitamin-b9_100g', 'vitamin-b12_100g', 'bicarbonate_100g',
-    'potassium_100g', 'chloride_100g', 'calcium_100g', 'phosphorus_100g', 'iron_100g',
-    'magnesium_100g', 'zinc_100g', 'copper_100g', 'manganese_100g', 'fluoride_100g',
-    'selenium_100g', 'chromium_100g', 'molybdenum_100g', 'iodine_100g',
-    'caffeine_100g', 'cocoa_100g', 'carbon-footprint_100g'
-]
-
-conn.close()
-chunk_size = 1000  
-
-similarity_df = pd.DataFrame(index=df['product_name'], columns=df['product_name'])
-for i in range(0, len(df), chunk_size):
-    chunk = df.iloc[i:i+chunk_size]
-    chunk_similarity_matrix = cosine_similarity(chunk[nutrient_columns])
-
-    chunk_similarity_df = pd.DataFrame(chunk_similarity_matrix, index=chunk['product_name'], columns=chunk['product_name'])
-    similarity_df.update(chunk_similarity_df)
-similarity_df = similarity_df.fillna(1.0)
-
-# * Similarities calculate by cosine similarities
-
-graph = nx.from_pandas_adjacency(similarity_df)
-node_degrees = graph.degree()
-sorted_nodes = sorted(node_degrees, key=lambda x: x[1], reverse=True)
-top_nodes = [node[0] for node in sorted_nodes[:10]]
-subgraph = graph.subgraph(top_nodes)
-pos = nx.spring_layout(subgraph)
-x, y = zip(*pos.values())
-edge_trace = go.Scatter(
-    x=[],
-    y=[],
-    line=dict(width=0.5, color='#888'),
-    hoverinfo='none',
-    mode='lines')
-
-for edge in subgraph.edges():
-    x0, y0 = pos[edge[0]]
-    x1, y1 = pos[edge[1]]
-    edge_trace['x'] += tuple([x0, x1, None])
-    edge_trace['y'] += tuple([y0, y1, None])
-
-node_trace = go.Scatter(
-    x=x,
-    y=y,
-    mode='markers+text',
-    hoverinfo='text',
-    text=list(subgraph.nodes()),
-    textposition='top center',
-    marker=dict(
-        showscale=False,
-        color='rgb(150,150,150)',
-        size=10,
-        line=dict(width=2, color='rgb(255,255,255)')))
-
-layout = go.Layout(
-    showlegend=False,
-    hovermode='closest',
-    margin=dict(b=20, l=5, r=5, t=40),
-    xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
-    yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))
-
-fig = go.Figure(data=[edge_trace, node_trace], layout=layout)
-fig.show()
-
-
-# ! Shortest Path
-
-G = nx.Graph()
-G.add_nodes_from(df['product_name'])
-
-# ! Add edges to the graph with weight as the modified similarity score(Reverse similarities score)
-for i in range(len(similarity_df)):
-    for j in range(i+1, len(similarity_df)):
-        if similarity_df.iloc[i,j] > 0:
-            similarity_score = similarity_df.iloc[i,j]
-            similarity_weight = 1 - similarity_score
-            G.add_edge(similarity_df.index[i], similarity_df.columns[j], weight=similarity_weight)
-
-# * Example
-start_node = 'Cheese twist'
-end_node = 'Pepperidge farm cookies'
-shortest_path = nx.shortest_path(G, start_node, end_node, weight='weight')
-
-print(shortest_path)